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Teaching MS&E 242H Investment Science Honors. Course Website. Syllabus. Introduction to the basic concepts of modern quantitative finance and investments. The course starts with developing the basic concepts under certainty. This includes arbitrage, term structure of interest rates and bond portfolio immunization. We then extend to a situation of uncertainty in one period. Topics: arbitrage; fundamental theorems of asset pricing; pricing measures; derivative securities; financial risk measures: basic theory, applications and estimation; mean-variance portfolio analysis, equilibrium and the capital asset pricing model. Group projects involving financial market data. Prerequisites: knowledge of basic probability, statistics and economics (MSE 120, 121, MATH51, ENGR 60, or equivalents). No prior knowledge of finance is assumed. Autumn.MS&E 347 Credit Risk: Modeling and Management. Course Website. Syllabus. Introduction to credit risk modeling, valuation and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Point processes and their compensators. Structural, incomplete information and reduced form approaches. Single name products: corporate bonds, equity, equity options, credit and equity default swaps, forwards and swaptions. Multi name modeling: index and tranche swaps, index and tranche options, collateralized debt obligations. Implementation, calibration and testing of models. Industry and market practice. Data and implementation driven group projects that focus on actual problems in the financial industry. Prerequisites: knowledge of stochastic processes at the level of MSE 321, 322 or equivalent, and knowledge of financial engineering at the level of MSE 342, MATH 180, MATH 240, F 622 or similar. Spring.MS&E 444 Investment Practice. Course Website. This is a projects course. If needed, there will be occasional lectures on background material. Students will work in small teams to tackle projects co-developed with EvA, a San Francisco based hedge fund. No prior knowledge in finance necessary, but strong quantitative skills for tackling market data-rich problems are key. At the end of the quarter, each team presents the project to the class and quantitative researchers and traders from the hedge fund. Spring. | |||||||||||
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